Scientists at University of California San Diego School of Medicine have developed an artificial intelligence (AI)-based strategy for discovering high-affinity antibody drugs. In the study, published January 28, 2023 in Nature Communications, researchers used the approach to identify a new antibody that binds a major cancer target 17-fold tighter than an existing antibody drug. The authors say the pipeline could accelerate the discovery of novel drugs against cancer and other diseases such as COVID-19 and rheumatoid arthritis. In order to be a successful drug, an antibody has to bind tightly to its target. To find such antibodies, researchers typically start with a known antibody amino acid sequence and use bacterial or yeast cells to produce a series of new antibodies with variations of that sequence.

Scientists at University of California San Diego School of Medicine have developed an artificial intelligence (AI)-based strategy for discovering high-affinity antibody drugs. In the study, published January 28, 2023 in Nature Communications, researchers used the approach to identify a new antibody that binds a major cancer target 17-fold tighter than an existing antibody drug. The authors say the pipeline could accelerate the discovery of novel drugs against cancer and other diseases such as COVID-19 and rheumatoid arthritis. In order to be a successful drug, an antibody has to bind tightly to its target. To find such antibodies, researchers typically start with a known antibody amino acid sequence and use bacterial or yeast cells to produce a series of new antibodies with variations of that sequence.

Machine learning helps develop optimal antibody drugs. Antibodies are not only produced by our immune cells to fight viruses and other pathogens in the body. For a few decades now, medicine has also been using antibodies produced by biotechnology as drugs. This is because antibodies are extremely good at binding specifically to molecular structures according to the lock-and-key principle. However, developing such antibody drugs is anything but simple.

Machine learning methods help to optimise the development of antibody drugs. This leads to active substances with improved properties, also with regard to tolerability in the body. Antibodies are not only produced by our immune cells to fight viruses and other pathogens in the body. For a few decades now, medicine has also been using antibodies produced by biotechnology as drugs. This is because antibodies are extremely good at binding specifically to molecular structures according to the lock- and-key principle.

Antibodies are not only produced by our immune cells to fight viruses and other pathogens in the body. For a few decades now, medicine has also been using antibodies produced by biotechnology as drugs. This is because antibodies are extremely good at binding specifically to molecular structures according to the lock-and-key principle. However, developing such antibody drugs is anything but simple. The basic requirement is for an antibody to bind to its target molecule in an optimal way.

IMAGE: Machine learning helps develop optimal antibody drugs. Antibodies are not only produced by our immune cells to fight viruses and other pathogens in the body. For a few decades now, medicine has also been using antibodies produced by biotechnology as drugs. This is because antibodies are extremely good at binding specifically to molecular structures according to the lock-and-key principle. However, developing such antibody drugs is anything but simple.